A powerful CLI tool to transform YouTube video transcripts into structured lecture notes, evidence-backed claims, and actionable checklists.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install bradford-lecture-analyzer
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install bradford-lecture-analyzer using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is YouTube Lecture Transcript Analyzer?
The YouTube Lecture Transcript Analyzer is a professional-grade tool designed to synthesize core structures, key arguments, and evidence from video-based lectures. By focusing strictly on transcript data, it eliminates hallucinations and provides a reliable basis for review, writing, or teaching. This skill is a core component of the Openclaw Skills ecosystem for users who need to process high-density information quickly.
It features a robust denoising engine that merges redundant ideas and removes verbal fillers, ensuring the final output is concise and professional. Whether you are a student, researcher, or developer, this skill helps you map out complex topics and identify blind spots in any presentation without spending hours watching the original footage.
YouTube Lecture Transcript Analyzer Use Cases
- Academic research and literature reviews based on video lectures.
- Rapid content synthesis for technical documentation and blogging.
- Extracting actionable business strategies and checklists from keynote speeches.
- Creating bilingual study guides or teaching materials from educational content.
- Validating specific technical claims by referencing original citations.
How YouTube Lecture Transcript Analyzer Works
- The user provides a YouTube URL or Video ID to the CLI script.
- The system fetches the transcript via the YouTube API using preferred language settings.
- A denoising process runs to merge repetitive viewpoints and correct verbal errors or slips.
- The analyzer categorizes information into four distinct types: Facts, Claims, Inferences, and Recommendations.
- Core concepts and their relationships are mapped out into a logical hierarchy.
- A structured Markdown report is generated, including direct citations for every key conclusion.
YouTube Lecture Transcript Analyzer Setup
To integrate this into your Openclaw Skills workflow, follow these steps:
- Install the required dependency:
pip install youtube-transcript-api
- Ensure your HTTP proxy is configured (default: 127.0.0.1:26739).
- Run the script with a YouTube URL:
python scripts/analyze_lecture.py <URL_OR_ID>
- For bilingual support, specify language priorities:
python scripts/analyze_lecture.py <URL> "zh-cn,en"
YouTube Lecture Transcript Analyzer Data Schema & Taxonomy
The skill produces a structured Markdown document with the following metadata taxonomy:
| Section | Contents |
|---|---|
| Lecture Map | Chronological structure divided into 3 to 6 major segments. |
| Q&A | 5 critical questions derived from the content with corresponding answers. |
| Categorization | Logical tagging of entries as Fact, Claim, Inference, or Recommendation. |
| Action List | Extracted executable tasks and checklists for the user. |
| Citations | Verifiable quotes (10-30 words) linked to every major conclusion. |
| Summary | Concise 200-word summaries provided in both Chinese and English. |
YouTube Lecture Transcript Analyzer Advanced Features
- Multi-language priority handling for international lecture support.
- Summary-only mode for high-speed scanning of video libraries.
- Intelligent denoising to filter out speech dysfluencies and repetitions.
- Blind spot identification to highlight areas requiring further verification.
- Counter-intuitive insight extraction to find unique 'highlights' in the talk.
SKILL.md
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